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*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 18 Dec 2010 12:52:59 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r.htm/, Retrieved Sat, 18 Dec 2010 13:54:01 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

31514 -9 0 8,3 1,2 27071 -13 4 8,2 1,7 29462 -18 5 8 1,8 26105 -11 -7 7,9 1,5 22397 -9 -2 7,6 1 23843 -10 1 7,6 1,6 21705 -13 3 8,3 1,5 18089 -11 -2 8,4 1,8 20764 -5 -6 8,4 1,8 25316 -15 10 8,4 1,6 17704 -6 -9 8,4 1,9 15548 -6 0 8,6 1,7 28029 -3 -3 8,9 1,6 29383 -1 -2 8,8 1,3 36438 -3 2 8,3 1,1 32034 -4 1 7,5 1,9 22679 -6 2 7,2 2,6 24319 0 -6 7,4 2,3 18004 -4 4 8,8 2,4 17537 -2 -2 9,3 2,2 20366 -2 0 9,3 2 22782 -6 4 8,7 2,9 19169 -7 1 8,2 2,6 13807 -6 -1 8,3 2,3 29743 -6 0 8,5 2,3 25591 -3 -3 8,6 2,6 29096 -2 -1 8,5 3,1 26482 -5 3 8,2 2,8 22405 -11 6 8,1 2,5 27044 -11 0 7,9 2,9 17970 -11 0 8,6 3,1 18730 -10 -1 8,7 3,1 19684 -14 4 8,7 3,2 19785 -8 -6 8,5 2,5 18479 -9 1 8,4 2,6 10698 -5 -4 8,5 2,9 31956 -1 -4 8,7 2,6 29506 -2 1 8,7 2,4 34506 -5 3 8,6 1,7 27165 -4 -1 8,5 2 26736 -6 2 8,3 2,2 23691 -2 -4 8 1,9 18157 -2 0 8,2 1,6 17328 -2 0 8,1 1,6 18205 -2 0 8,1 1,2 20995 2 -4 8 1,2 17382 1 1 7,9 1,5 9367 -8 9 7,9 1,6 31124 -1 -7 8 1,7 26551 1 -2 8 1,8 30651 -1 2 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Inschrijvingen[t] = + 29192.3025763464 + 174.389476389964Consumentenvertrouwen[t] + 42.8784606729749Evolutie_consumentenvertrouwen[t] -649.244649320328Totaal_Werkloosheid[t] + 135.258379303979Algemene_index[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	29192.3025763464	8666.019076	3.3686	0.00117	0.000585
Consumentenvertrouwen	174.389476389964	101.417828	1.7195	0.089436	0.044718
Evolutie_consumentenvertrouwen	42.8784606729749	184.355444	0.2326	0.816685	0.408342
Totaal_Werkloosheid	-649.244649320328	1024.484668	-0.6337	0.528089	0.264044
Algemene_index	135.258379303979	458.341125	0.2951	0.768688	0.384344

Multiple Linear Regression - Regression Statistics
Multiple R	0.208606962378333
R-squared	0.0435168647527153
Adjusted R-squared	-0.00491266108259025
F-TEST (value)	0.898560619831449
F-TEST (DF numerator)	4
F-TEST (DF denominator)	79
p-value	0.468975153690604
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5721.3399735494
Sum Squared Residuals	2585964756.34181

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31514	22396.3767546427	9117.62324535732
2	27071	22002.8863463588	5068.11365364117
3	29462	21317.1921928765	8144.80780712354
4	26105	22047.7239506713	4057.27604932866
5	22397	22738.0394119603	-341.039411960254
6	23843	22773.4403451716	1069.55965482840
7	21705	21868.0317448930	-163.031744893031
8	18089	21978.0714431672	-3889.07144316724
9	20764	22852.8944588151	-2088.89445881513
10	25316	21768.0033898223	3547.99661017771
11	17704	22563.3954383366	-4859.39543833664
12	15548	22792.4009786686	-7244.40097866855
13	28029	22978.6347930930	5050.36520690698
14	29383	23394.6391576868	5988.36084231324
15	36438	23514.9446963981	12923.0553036019
16	32034	23925.2791822346	8108.72081776539
17	22679	23908.8329504365	-1229.83295043654
18	24319	24441.7156797373	-122.715679737267
19	18004	23277.5257097891	-5273.5257097891
20	17537	23017.3598980102	-5480.35989801022
21	20366	23076.0651434954	-2710.06514349537
22	22782	23061.3004115932	-279.300411593193
23	19169	23042.3203640533	-3873.32036405328
24	13807	23025.4509403741	-9218.45094037406
25	29743	22938.4804711830	6804.51952881703
26	25591	23308.6665671931	2282.3334328069
27	29096	23701.3666195130	5394.63338048696
28	26482	23503.9079140399	2978.09208596005
29	22405	22610.5533888599	-205.553388859929
30	27044	22537.2349064077	4506.76509359226
31	17970	22109.8153277443	-4139.8153277443
32	18730	22176.4018785293	-3446.40187852926
33	19684	21706.7621142647	-2022.76211426468
34	19785	22359.482430226	-2574.48243022599
35	18479	22563.6924814093	-4084.69248140928
36	10698	23022.5111324634	-12324.5111324634
37	31956	23549.642594368	8406.35740563198
38	29506	23562.5937454821	5943.40625451787
39	34506	23095.4258370774	11410.5741629226
40	27165	23203.8034494987	3961.19655050127
41	26736	23140.5604844626	3595.43951553741
42	23691	23735.0435069895	-44.0435069895003
43	18157	23736.1309060261	-5579.13090602614
44	17328	23801.0553709582	-6473.05537095817
45	18205	23746.9520192366	-5541.95201923658
46	20995	24337.9205470366	-3342.92054703657
47	17382	24483.4253527347	-7101.4253527347
48	9367	23270.4735885392	-13903.4735885392
49	31124	23753.7459254997	7370.25407450026
50	26551	24330.4430195749	2220.55698042506
51	30651	24218.1023744189	6432.89762558105
52	25859	24461.9540352919	1397.04596470807
53	25100	24717.7336224550	382.266377545033
54	25778	24910.8449313981	867.155068601858
55	20418	24423.6968824255	-4005.69688242549
56	18688	24295.7003614354	-5607.70036143542
57	20424	24501.6986472801	-4077.69864728014
58	24776	24727.8905285099	48.1094714900885
59	19814	23944.8727446767	-4130.87274467675
60	12738	24067.9703288306	-11329.9703288306
61	31566	23965.6410268781	7600.35897312191
62	30111	24417.7919204776	5693.20807952243
63	30019	24849.4074007358	5169.59259926422
64	31934	24556.0095217352	7377.99047826484
65	25826	24451.5054217094	1374.49457829064
66	26835	23924.1836367837	2910.81636321631
67	20205	22809.8369427498	-2604.83694274976
68	17789	22789.6239333217	-5000.62393332174
69	20520	23411.6265837602	-2891.62658376019
70	22518	22726.6354655192	-208.635465519243
71	15572	21704.4126894602	-6132.41268946023
72	11509	20833.0847544963	-9324.0847544963
73	25447	20802.0312178149	4644.96878218513
74	24090	20241.5027117941	3848.49728820591
75	27786	19894.152976007	7891.84702399298
76	26195	20351.9484022371	5843.0515977629
77	20516	20956.6778511582	-440.677851158166
78	22759	21057.2189184493	1701.78108155074
79	19028	20695.9821127326	-1667.98211273261
80	16971	21519.8102792170	-4548.81027921703
81	20036	21852.8266213974	-1816.82662139736
82	22485	21878.2162114052	606.783788594771
83	18730	22220.0923235398	-3490.09232353981
84	14538	21442.6996582943	-6904.69965829428

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.626954474016643	0.746091051966714	0.373045525983357
9	0.476761926570291	0.953523853140583	0.523238073429709
10	0.347829655297525	0.695659310595049	0.652170344702475
11	0.243345717466518	0.486691434933036	0.756654282533482
12	0.207691324350087	0.415382648700173	0.792308675649913
13	0.336362342286512	0.672724684573023	0.663637657713488
14	0.311955923895477	0.623911847790955	0.688044076104523
15	0.432704932205831	0.865409864411661	0.56729506779417
16	0.46989062159706	0.93978124319412	0.53010937840294
17	0.385109662848931	0.770219325697863	0.614890337151069
18	0.302795889648185	0.605591779296369	0.697204110351815
19	0.264729141569837	0.529458283139674	0.735270858430163
20	0.207828996301787	0.415657992603574	0.792171003698213
21	0.153383747559006	0.306767495118012	0.846616252440994
22	0.151624334757333	0.303248669514666	0.848375665242667
23	0.111751741953842	0.223503483907685	0.888248258046157
24	0.152531250233664	0.305062500467327	0.847468749766336
25	0.254599264275674	0.509198528551348	0.745400735724326
26	0.273383150312409	0.546766300624818	0.726616849687591
27	0.375906597804290	0.751813195608579	0.624093402195711
28	0.331563223479362	0.663126446958725	0.668436776520638
29	0.273545197185709	0.547090394371418	0.726454802814291
30	0.286389009077301	0.572778018154603	0.713610990922699
31	0.235954084242841	0.471908168485683	0.764045915757159
32	0.191075961355059	0.382151922710118	0.808924038644941
33	0.148451556723700	0.296903113447399	0.8515484432763
34	0.117518755132131	0.235037510264261	0.882481244867869
35	0.0991478039383964	0.198295607876793	0.900852196061604
36	0.226528677187785	0.453057354375571	0.773471322812215
37	0.331077471438805	0.66215494287761	0.668922528561195
38	0.328195227558706	0.656390455117411	0.671804772441294
39	0.512021696649853	0.975956606700294	0.487978303350147
40	0.485529141133595	0.97105828226719	0.514470858866405
41	0.474775649925768	0.949551299851537	0.525224350074232
42	0.415791524883852	0.831583049767704	0.584208475116148
43	0.479235874995714	0.958471749991427	0.520764125004286
44	0.545502510524226	0.908994978951547	0.454497489475774
45	0.579657662896902	0.840684674206196	0.420342337103098
46	0.552575685721429	0.894848628557141	0.447424314278571
47	0.59523178985504	0.80953642028992	0.40476821014496
48	0.82784369377514	0.34431261244972	0.17215630622486
49	0.844666731152888	0.310666537694224	0.155333268847112
50	0.808704248284584	0.382591503430831	0.191295751715416
51	0.836603553790458	0.326792892419084	0.163396446209542
52	0.799486461144434	0.401027077711133	0.200513538855566
53	0.755039883715798	0.489920232568404	0.244960116284202
54	0.706809426158032	0.586381147683937	0.293190573841968
55	0.660157234560559	0.679685530878883	0.339842765439441
56	0.641003214764974	0.717993570470053	0.358996785235026
57	0.601189655259416	0.797620689481169	0.398810344740584
58	0.530297517311191	0.939404965377618	0.469702482688809
59	0.482552589172403	0.965105178344806	0.517447410827597
60	0.722592008417032	0.554815983165936	0.277407991582968
61	0.788886255286006	0.422227489427988	0.211113744713994
62	0.776364320339695	0.447271359320611	0.223635679660305
63	0.765822147427541	0.468355705144918	0.234177852572459
64	0.854157674685922	0.291684650628157	0.145842325314078
65	0.833761307905326	0.332477384189348	0.166238692094674
66	0.89681956004271	0.206360879914581	0.103180439957291
67	0.850757148219773	0.298485703560454	0.149242851780227
68	0.82132112498933	0.357357750021342	0.178678875010671
69	0.748773357031045	0.50245328593791	0.251226642968955
70	0.84395048761983	0.312099024760339	0.156049512380170
71	0.792002364812806	0.415995270374389	0.207997635187194
72	0.958848404318428	0.082303191363145	0.0411515956815725
73	0.921071674889003	0.157856650221994	0.0789283251109972
74	0.85224383980844	0.295512320383121	0.147756160191561
75	0.842726338195866	0.314547323608267	0.157273661804134
76	0.996925372529832	0.00614925494033618	0.00307462747016809

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0144927536231884	NOK
5% type I error level	1	0.0144927536231884	OK
10% type I error level	2	0.0289855072463768	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/10gqp41292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/10gqp41292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/1kyad1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/1kyad1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/2kyad1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/2kyad1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/3kyad1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/3kyad1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/4d7rg1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/4d7rg1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/5d7rg1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/5d7rg1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/6d7rg1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/6d7rg1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/75h8j1292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/75h8j1292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/8gqp41292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/8gqp41292676770.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/9gqp41292676770.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676830c65w1sanl1g8v2r/9gqp41292676770.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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